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Quantum tunneling

As explained in Optimization Algorithms: AI techniques for design, planning, and control problems

A quantum phenomenon in which a particle or wave function penetrates a potential barrier despite having less energy than the barrier height.

Quantum tunneling is the ability of a particle or wave function to penetrate a potential barrier even when its total energy is below the barrier height. The probability is nonzero when the barrier is sufficiently thin. section 5.2.2

In optimization, quantum annealing applies this effect to an energy landscape. Rather than relying only on thermal jumps, it can tunnel through barriers and escape local minima while searching for an optimal or near-optimal solution. section 5.2.2

The treatment distinguishes quantum annealing from simulated annealing through these mechanisms:

  • Simulated annealing uses thermal jumps to move a search particle out of local valleys. section 5.2.2
  • Quantum annealing initializes candidate states with equal weights, then changes their quantum-mechanical amplitudes gradually and in parallel. section 5.2.2
  • The resulting search may cross energy barriers instead of merely walking through the landscape. section 5.2.2
Simulated annealing versus quantum annealing

Compare the contrasting search mechanisms of simulated annealing and quantum annealing. section 5.2.2

How Optimization Algorithms: AI techniques for design, planning, and control problems covers it

How deep it goes
Defines barrier penetration and sketches quantum-annealing mechanics, but stops short of a detailed physical derivation.
Examples
Uses thermal-versus-quantum search behavior, candidate states, energy barriers, and a D-Wave implementation as concrete reference points.
What it assumes
Contrasts quantum effects with thermal jumps without requiring a stated mathematical or programming prerequisite.
What you can do after
Evaluate how quantum tunneling could help an optimization search cross barriers and avoid local minima.
How it connects
Connects quantum tunneling with quantum annealing, simulated annealing, local minima, energy landscapes, and optimization.

This explainer was generated by Manning's knowledge graph from the text of Optimization Algorithms: AI techniques for design, planning, and control problems. The section links point to the parts of the book it draws on.